Abstract
We present a high-resolution atmospheric inversion system combining a Lagrangian Particle Dispersion Model (LPDM) and the Weather Research and Forecasting model (WRF), and test the impact of assimilating meteorological observation on transport accuracy. A Four Dimensional Data Assimilation (FDDA) technique continuously assimilates meteorological observations from various observing systems into the transport modeling system, and is coupled to the high resolution CO2 emission product Hestia to simulate the atmospheric mole fractions of CO2. For the Indianapolis Flux Experiment (INFLUX) project, we evaluated the impact of assimilating different meteorological observation systems on the linearized adjoint solutions and the CO2 inverse fluxes estimated using observed CO2 mole fractions from 11 out of 12 communications towers over Indianapolis for the Sep.-Nov. 2013 period. While assimilating WMO surface measurements improved the simulated wind speed and direction, their impact on the planetary boundary layer (PBL) was limited. Simulated PBL wind statistics improved significantly when assimilating upper-air observations from the commercial airline program Aircraft Communications Addressing and Reporting System (ACARS) and continuous ground-based Doppler lidar wind observations. Wind direction mean absolute error (MAE) decreased from 26 to 14 degrees and the wind speed MAE decreased from 2.0 to 1.2 m s–1, while the bias remains small in all configurations (< 6 degrees and 0.2 m s–1). Wind speed MAE and ME are larger in daytime than in nighttime. PBL depth MAE is reduced by ~10%, with little bias reduction. The inverse results indicate that the spatial distribution of CO2 inverse fluxes were affected by the model performance while the overall flux estimates changed little across WRF simulations when aggregated over the entire domain. Our results show that PBL wind observations are a potent tool for increasing the precision of urban meteorological reanalyses, but that the impact on inverse flux estimates is dependent on the specific urban environment.
Highlights
Inversion of atmospheric tracers (Tarantola 2005) is a widely used method to determine the surface fluxes of greenhouse gases (GHGs) such as carbon dioxide (CO2) (Tans et al 1990; Enting and Mansbridge 1989) using observations from various observing platforms such as towers (Richardson et al 2012, Andrews et al 2014, Miles et al 2016, Richardson et al 2016), aircraft (e.g. Gerbig et al 2003), ground-based remote sensing (e.g. Wunch et al 2010) and more recently satellite measurements (Crisp et al 2004)
Since in four dimensional data assimilation (FDDA) the impact of surface observations is limited to the lowest portion of atmosphere up to the model-simulated planetary boundary layer (PBL) top (Rogers et al 2013), it is anticipated that assimilating additional upper air observations such as Lidar and ACARS observations (Figure 2b, 2c, 2d) can cause additional improvements in the Weather Research and Forecasting (WRF) model solutions so that the transport error in the inversion system is further reduced
Random errors in meteorological variables propagates into the flux solution as additional posterior uncertainties but do not create any systematic errors in the fluxes. While this ensemble of simulations is not calibrated to meteorological observations (e.g. Grimit and Mass, 2007), and does not necessarily represent the true transport errors in the simulation, we show here that for this experiment, the different observations used in the WRF-FDDA simulations do affect the spatial attribution of flux corrections but have a limited impact on total inverse emissions over the entire domain
Summary
Inversion of atmospheric tracers (Tarantola 2005) is a widely used method to determine the surface fluxes of greenhouse gases (GHGs) such as carbon dioxide (CO2) (Tans et al 1990; Enting and Mansbridge 1989) using observations from various observing platforms such as towers (Richardson et al 2012, Andrews et al 2014, Miles et al 2016, Richardson et al 2016), aircraft (e.g. Gerbig et al 2003), ground-based remote sensing (e.g. Wunch et al 2010) and more recently satellite measurements (Crisp et al 2004). Experimental design In order to evaluate the effect of assimilating various observations, as shown, four different WRF configurations (or experiments) are conducted and results of both meteorological fields and posterior CO2 fluxes are compared among the four experiments: 1) NOFDDA – No data assimilation of any form is applied, and WRF is purely driven by the North America Regional Reanalysis (NARR), an analysis product that is a combination of model background and observations but on a coarse spatial and temporal scale (i.e., 32 km); 2) FDDA_WMO – Only standard WMO hourly surface (winds) and 12-hourly upper-air observations (winds, temperature and water vapor) are assimilated; 3) FDDA_WMO_Lidar – In addition to WMO observations, wind profiles from the local INFLUX lidar are assimilated; and 4) FDDA_WMO_Lidar_ACARS – In addition to the WMO and lidar observations, the ACARS observations are assimilated.
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